Validity of learning styles - Remi Tremblay and Piers Maclearn
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Validity of learning styles - Remi Tremblay and Piers Maclearn

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This presentation was presented to the Multichannel Learning Systems (MLS) working group in May 2013. It goes through the Myths surrounding the use of Learning Styles.

This presentation was presented to the Multichannel Learning Systems (MLS) working group in May 2013. It goes through the Myths surrounding the use of Learning Styles.

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  • For more than 30 years, the notion that teaching methods should match a student’s particular learning style has exerted a powerful influence on education. The long-standing popularity of the learning styles movement has in turn created a thriving commercial market amongst researchers, educators, and the general public.The wide appeal of the idea that some students will learn better when material is presented visually and that others will learn better when the material is presented verbally, or even in some other way, is evident in the vast number of learning-style tests and teaching guides available for purchase and used in schools. But does scientific research really support the existence of different learning styles, or the hypothesis that people learn better when taught in a way that matches their own unique style?
  • Introduction – Remi and PiersRef:Les Howles – University of Wisconsin Madison -
  • Exercise – Thinking about our current practice
  • CE – Feeling or Sensing (non affective)RO – WatchingAC – Thinking, analysingAE - Doing
  • We can address Felder and Solomon seperatelyN.B. Most learning styles are identified through self-report questionnairesLearning Styles:What the Research Says andHow to Apply it to Designing E-LearningLes HowlesSenior e-Learning ConsultantUniversity of Wisconsin - Madison Madison, Wisconsin howles@doit.wisc.edu608.265.5045
  • Ref: Learning Styles: How to Apply the Latest Research to Designing e-Learning It seems that advocates of learning styles have never heard of the history of ATI research, which attempted to provide a database for adapting instruction to student characteristics and found many thorny problems. It is probably fair to say that the popularity of adapting instruction to learning styles is matched only by the utter absence of support for this idea. Claims for adapting instruction to learning styles, of course, assume that there are stable, replicable interactions between measures of learning styles and instructional methods. A number of reviews of ATI research (Tobias, 1989; Cornobach and Snow, 1986; Gustaffson and Undheim, 1996) have reached fairly similar conclusions about the types of interactions that have been verified by research. These reviews suggest that students with limited prior knowledge of a domain, or lower ability, require substantial instructional support in such forms as better organization of the content, increased feedback, provision of prompts, and similar instructional augmentations in order to learn optimally. Students with higher levels of prior knowledge, or higher ability, are optimally instructed with lower levels of instructional support. Unless I, and the other reviewers of research in this area, have missed the publication of tons of replicated findings, there is no evidence of stable interactions between learning styles and instructional methods. Why then do otherwise knowledgeable educators and educational researchers persist in making unverified claims for learning styles? I can only conclude that they adhere to what Jeanne Chall (2000) in her last book called a romantic, as opposed to rational, view of education. Chall cites other romantic notions that have little verified empirical support such as the whole-language approach to reading instruction, open education, and discovery learning, to name only a few. Sometimes an idea may appear so logical, and/or so deeply related to the values held by individuals, that it becomes an article of faith. Believers cling to their fancies irrespective of research findings. I wish they would develop a similar fixation about the Brooklyn Bridge, because I would love to sell it to them again and again…[1].
  • Ref: Learning Styles: Concepts and Evidence Harold Pashler, Mark McDaniel, Doug Rohrer, and Robert Bjork - According to a major report published in Psychological Science in the Public Interest, a journal of the Association for Psychological Science. The report demonstrated that nearly all of the studies that purport to provide evidence for learning styles fail to satisfy key criteria for scientific validity. Any experiment designed to test the learning-styles hypothesis would need to classify learners into categories and then randomly assign the learners to use one of several different learning methods, and the participants would need to take the same test at the end of the experimentRef: Learning Styles: Concepts and Evidence, 2008Result(s): The available research on the topic offers little evidence to support the development of curriculum, instruction, and assessment packages based on learning styles. The study suggested that validation of learning style instruction begins with rigorous research designed to meet the following criteria:Students must be divided into two groups based on an assessment of learning styles.The students from the two groups must be randomly assigned to receive training in one of multiple instructional methods designed to match a learning style.All of the students must be tested at the end of the instructional period.Finally, the study must be able to identify a statistically significant difference in student performance based on learning style and instructional methods constructed to match the learning style.Despite the existence of a large number of studies on the topic of learning styles, only three studies were identified as meeting the rigorous research criteria: Massa and Mayer (2006)Thompson, Thomas, and Thomas (2009)Constantinidou and Baker (2002)The authors concluded the following based on a review of the literature: There is ample evidence that children and adults express preferences for how information should be presented. There is evidence that people differ in their aptitudes for different kinds of thinking and for processing different types of information. There is virtually no evidence to justify incorporating learning style assessments into educational practice.Implication(s): The study provided the following recommendations to educators and parents:Given the pressing need to make timely improvements in the education system, interventions should be selected based on what has been proved to work.Given the limited resources available to schools, money should be spent on evidence-based educational practices. Given the lack of sound studies of learning styles, further research on learning styles should be conducted, but such research needs to employ rigorous research methods.The study pointed out that even if the evidence had documented the efficacy of using learning styles interventions, which was clearly not its conclusion, the impact would need to be large and robust to justify taking the practice to scale. A small impact would result in a practice that did not meet the goal of being cost effective. Interventions built around learning styles are potentially expensive in terms of dollars and teacher time. Students must be assessed, grouped by learning style, and given customized instruction designed to match each style.There is a prize of $1000 (US dollars) to the first person or group who can prove that taking learning styles into account in designing instruction can produce meaningful learning benefits. an e-learning program that utilizes learning-style information outperform an e-learning program that doesn't utilize such information by 10% or more on a realistic test of learning, even it is allowed to cost up to twice as much to build? $1,000 says it just doesn't happen in the real-world of instructional design. $1,000 says we ought to stop wasting millions trying to cater to this phantom curse.
  • the extent that teachers use scientific theories about the mind to inform their practice, doesn’t it make sense to use theories that scientists are pretty sure are right?
  • What would Felder Do? Creator of the Felder-Solomon Index of learning styles which has demonstrated a level of validity with Engineering Students. argument suggests what the authors consider to be the most important application of learning styles, which is designing effective instruction. Having a framework for identifying the different types of learners can help an instructor formulate a teaching approach that addresses the needs of all students. Moreover, determining the learning style profile of a class using an instrument such as the Index of Learning Styles (without being overly concerned about which student has which preferences) provides additional support for effective instructional design. For example, knowing that a large majority of students in a class are sensing and visual learners can-and should-motivate an instructor to find concrete and visual ways topresent material that might normally be presented entirely abstractly and verbally.
  • Ref: ARE LEARNING STYLES INVALID? (HINT: NO!) Richard M. Felder North Carolina State University
  • Ref: ARE LEARNING STYLES INVALID? (HINT: NO!) Richard M. Felder North Carolina State UniversityPrinciples of InstructionGood ISDKnown to UnknownSimple to Complex
  • Ref: ARE LEARNING STYLES INVALID? (HINT: NO!) Richard M. Felder North Carolina State UniversityThe following points may help the reader to maintain a realistic perspective on this unfortunately controversial subject.
  • Ref: ARE LEARNING STYLES INVALID? (HINT: NO!) Richard M. Felder North Carolina State University
  • Mayer, 2005 – Knowledge construction is a sense-making activity in which the learner seeks to build a coherent mental representation from the presented material. Unlike information – which is an objective commodity that can be moved from one mind to another – knowledge is personally constructed by the learner and cannot be delivered in exact from one mind to another. This is why two learners can be presented with the same multimedia message and come away with different learning outcomes.Second, according to the knowledge construction view, the learner’s job is to make sense of the presented material; thus, the learner is an active sense maker who experiences a multimedia presentation and tries to integrate the presented material into a coherent mental representation.Third, the teacher’s job is to assist the learner in this sense-making process; thus the teacher is a cognitive guide who provides needed guidance to support the learner’s cognitive processing. Fourth, the goal of multimedia presentations is not only to present information, but also to provide guidance for how to process the presented information – that is, for determining what to pay attention to, how to mentally organise it, and how to relate it to prior knowledge. Finally , the guiding metaphor is that of multimedia as a helpful communicator. According to this metaphor, multimedia is a sense-making guide, that is, an aid to knowledge construction. See also knowledge construction view is based on three principles from research in cognitive science:Dual Channels – people have separate channels for processing visual/pictorial material;Limited capacity – people can actively process only a few pieces of information in each channel at one time; and …Active processing – learning occurs when people engage in appropriate cognitive processing during learning, such as attending to relevant material, organising the material into a coherent structure, and integrating it with what they already know.
  • See Tversky (1993) and for discussion of the above simplification … which is nonetheless helpful at this point.This is only a ‘snapshot’ and in theory becomes ‘correct prior knowledge’ which may change and be integrated with new knowledge … in the same way that misconceptions are corrected. It helps others concept of the topic – Clark and Lewis become the ‘guides’.
  • prior knowledge (black), newly acquired knowledge (blue), and corrected misconceptions (red).
  • (Clark & Mayer, 2003)
  • Ref: Learning Styles: How to Apply the Latest Research to Designing e-Learning We know that using these multimedia principles will help to create a more effective instructional design. However, we know that students also vary greatly from one to another. What are some of the other empirically validated differentiators should we be looking at? Remi will expand. Similar to learning “styles,” these four learner characteristics can be represented on a bipolar scale as illustrated below. Where individuals or groups of learners fall on each scale can often be determined by asking a few simple questions that relate the content or performance to be learned with each of the learner characteristics.
  • Ref: Learning Styles: How to Apply the Latest Research to Designing e-Learning At first glance, the learner characteristics below may not be as appealing as global learner traits suchas “styles.” However, research in learning psychology is unequivocal about the significant impact thatmost of these have on learner success in specific learning situations.Individuals with greater pre-existing knowledge of a topic understand and remember more than those with more limited prior knowledge(Committee on Developments in the Science of Learning, National Research Council, 1999; Schneider & Pressley, 1997). Moreover,prior knowledge within specific domains benefits students’ learning and achievement (Alexander & Judy, 1988; Dochy, Segers, &Buehl, 1999). This conclusion has been supported by studies of a variety of academic content domains, including physics and mathematics (Hudson & Rottmann, 1981), writing ability and text processing (McCutcheon, 1986), economics (Dochy, 1992), and computer programming (Klahr & Carver, 1988), with students ranging from elementary grades to graduate school.However, if prior knowledge is inaccurate, incomplete, or misleading, it can hinder understanding or learning new informationFrom available research on motivation and academic performance, it became quite evident that motivational constructs do in fact impact the academic performance of students. There are studies documenting the correlation of the Scholastic Aptitude Test, American College Testing (Ward, 1993), Mathematics (Carpenter, 1993; Ward, 1993; Gist, 1996), High School Grade Point Average (Price and Kim, 1976; Carpenter, 1993) and College Entrance Examination (Price and Kim, 1976) scores and the performance of college students. Also well documented are studies in the areas of arts and sciences, psychology, philosophy, and natural sciences.
  • Ref: Learning Styles: How to Apply the Latest Research to Designing e-Learning Bandura (1977) hypothesized that self-efficacy affects an individual's choice of activities, effort, and persistence. People who have a low sense of efficacy for accomplishing a task may avoid it; those who believe they are capable should participate readily. Individuals who feel efficacious are hypothesized to work harder and persist longer when they encounter difficulties than those who doubt their capabilities. Self-efficacy theory postulates that people acquire information to appraise efficacy from their performance accomplishments, vicarious (observational) experiences, forms of persuasion, and physiological indexes. An individual's own performances offer the most reliable guides for assessing efficacy. Successes raise efficacy and failure lowers it, but once a strong sense of efficacy.
  • An emphasis on self-regulated learning in the military community (U.S. Army Training & Doctrine Command, 2011) has highlighted a need for point-of-need training in environments where human tutors are either unavailable or impractical. Computer-Based Tutoring Systems (CBTS) have been shown to be as effective as expert human tutors (VanLehn, 2011) in one-to-one tutoring in well-defined domains (e.g., mathematics or physics) and significantly better than traditional classroom training environments. CBTS have demonstrated significant promise, but fifty years of research have been unsuccessful in making CBTS ubiquitous in military training or the tool of choice in our educational system. Why? The availability and use of CBTS have been constrained by their high development costs, their limited reuse, a lack of standards, and their inadequate adaptability to the needs of learners (Picard, 2006). Their application to military domains is further hampered by the complex and often ill-defined environments in which our military operates today. CBTS are often built as domain-specific, unique, one-of-a-kind, largely domain-dependent solutions focused on a single pedagogical strategy (e.g., model tracing or constraint-based approaches) when complex learning domains may require novel or hybrid approaches. The authors posit that a modular CBTS framework and standards could enhance reuse, support authoring and optimization of CBTS strategies for learning, and lower the cost and skillset needed for users to adopt CBTS solutions for military training and education. This paper considers the design and development of a modular CBTS framework called the Generalized Intelligent Framework for Tutoring (GIFT).
  • Ref: -  Use of Evidence-based Strategies to Enhance the Extensibility of Adaptive Tutoring TechnologiesFew open-source components, methods or standards for CBTSFew authoring tools or standards to promote reuseCBTS do not support small unit training/tutoring experiencesU.S. Air Education and Training Command (2008). On Learning: The Future of Air Force Education and Training. Randolph AFB, TX. 
  • Ref: -  Use of Evidence-based Strategies to Enhance the Extensibility of Adaptive Tutoring TechnologiesRef:'s_2_Sigma_ProblemBloom's 2 sigma problem refers to an educational phenomenon observed by educational psychologist Benjamin Bloom and initially reported in 1984 in the journal "Educational Researcher". Bloom found that the average student tutored one-to-one using mastery learning techniques performed two standard deviations better than students who learn via conventional instructional methods[1]—that is, "the average tutored student was above 98% of the students in the control class".[2] Additionally, the variation of the students' achievement changed: "about 90% of the tutored students ... attained the level of summative achievement reached by only the highest 20%" of the control class.[3] Bloom's graduate students J. Anania and A. J. Burke conducted studies of this effect at different grade levels and in different schools, observing students with "great differences in cognitive achievement, attitudes, and academic self-concept".[4]
  • Ref: – Dr Dexter FletcherEffect Size RefA descriptive (not inferential) statistic often used to estimate the magnitude of an effect (e.g., experimental treatment). It may be calculated as:Cohen’sd =Mean Group 1 –Mean of Group 2 / “Pooled” Standard DeviationD>.80 Very LargeD .60 - .79 Large(IDA 2011 Document NS D4260)Comparison:• 4 weeks of then available DT (N = 20) 16 weeks of Integrated Learning Environment (ILE) CBT graduates (N = 31)ILE and DT instructors (N = 10)Measure:152-item written knowledge test covering DT materialResults:DT ILE (d = 2.81)aDT Instructors (d = 1.26)aInstructorsILE (d = 1.25)aa(p < 0.01)
  • Ref:‎A deeper understanding of the learner’s behaviors, traits, and preferences (learner data) collected through performance, physiological and behavioral sensors and surveys will allow for more accurate evaluation of the learner’s states (e.g., engagement level, confusion, frustration) which will result in a better and more persistent model of the learner. To enhance the adaptability of the CBTS, methods are needed to accurately classify learner states (e.g., cognitive, affective, psychomotor, social) and to select optimal instructional strategies given the learner’s existing states. A more comprehensive learner model will allow the CBTS to adapt more appropriately to address the learner’s needs by changing the instructional strategy (e.g., content, flow or feedback). An instructional strategy that is better aligned to the learners’ needs is more likely to positively influence their learning gains. Adaptive Tutoring research goals that are driving future GIFT development include:an ontology to represent the knowledge and concepts of CBTS, their relationships and their interactionsautomated tools and processes to author CBTS, their models, and componentsautomated delivery methods for tailored instruction to individuals and small teamsa set of reusable, domain-independent modules that include standard processes and structuresa testbed methodology to support the systematic comparison and assessment of CBTS, their components, and instructional method
  • Ref: -  Use of Evidence-based Strategies to Enhance the Extensibility of Adaptive Tutoring TechnologiesRef:‎The GIFT functional elements include the components, modules, models, messages, databases, libraries and interfaces that support the authoring, instructional and analysis processes within GIFT. For each module to interact seamlessly and adapt to new information, a JAVA-based framework has been constructed to facilitate the communication of GIFT modules. The SOA allows GIFT to be expanded by adding new modules and standard messages. GIFT is also able to run on physically separate computers/mobile devices and to be accessed by multiple learners simultaneously to support concurrent training sessions or distributed team training. JBoss, a JAVA-based application server, has been implemented in GIFT to publish and receive information not initially expected in the design, provided that the information recipient is compatible. Messages are JavaScript Object Notation (JSON) encoded.
  • Ref: -  Use of Evidence-based Strategies to Enhance the Extensibility of Adaptive Tutoring TechnologiesRef:‎ The pedagogical module queries state data from the learner module (learner state) and the domain module (performance assessment and associated feedback). It uses learner state and performance to determine the content, order and flow of instruction. Visual and audio stimuli are fed to the user-tutor interface based on recommendations by the pedagogical module. The question that the pedagogical module is tasked to answer is: “given a learner is in the following state, what is the recommended course of action?” Traditional machine learning methods such as Bayesian networks or decision trees can be used in making the decision regarding pedagogical strategy. Pedagogical strategies are in place to make decisions about what to do next when multiple options are available (Dabbagh, 2005). They can manipulate elements within a training scenario; provide hints or feedback; and change the pace and difficulty of interaction (Wulfeck, 2009). To support the development of optimized instructional strategies and tactics, GIFT is heavily grounded in learning theory, tutoring theory and motivational theory. Learning theory applied in GIFT includes cognitive learning (Anderson & Krathwohl, 2001), affective learning (Krathwohl, Bloom, and Masia, 1964; Goleman, 1995), psychomotor learning (Simpson, 1972), and social learning (Sottilare, Holden, Brawner, and Goldberg, 2011; Soller, 2001). Aligning with our goal to model expert human tutors, GIFT considers the INSPIRE model of tutoring success (Lepper, Drake, and O'Donnell-Johnson, 1997) and the tutoring process defined by Person, Kreuz, Zwaan, and Graesser (1995) in the development of GIFT instructional strategies and tactics.
  • Generalized Intelligent Framework for Tutoring (GIFT) Integration - vMedicFor the purpose of this research, the tasks/objectives defined within vMedic will drive the Intelligent Tutoring behaviors within GIFT which in turn, will drive a number of instructional interventions within“vMedic”. “vMedic” is being modified to accommodate a number of prescribed instructional interventions for each role of a particular scenario. These instructional interventions may be incorporated into the vignette as part of the “game-play”. However, Adobe Flash was used to implement the user interface in vMedic which also allows for the integration of motion graphics, video files, and loading 2D content at run-time for a much more dynamic experience.  This will facilitate the implementation of some of these instructional strategies in the form of illustrative videos, tutorials, and lessons directly from within the simulation. Key Takeaway: The development of these interface specifications will enable commercial games to more easily integrate with Intelligent Tutoring Systems to promote centrally managed instruction that transcends the traditional boundaries of engine specific tools and formats. Currently, there is a lot of activity within the services to identify areas of commonality between scenario-based training platforms such as learning games and simulations. This work could generate some potential standard protocols for intelligent tutoring that will help promote interoperability of simulations and student performance data while reducing costs and redundancy of efforts.
  • Ref: Learning Styles: How to Apply the Latest Research to Designing e-Learning
  • Ref: Learning Styles: How to Apply the Latest Research to Designing e-Learning


  • 1. Validity of Learning Styles “Questioning the validity of learning styles is not a denial of individual learner differences” LCdr. Remi Tremblay – Canadian Defence Academy Mr. Piers MacLean - Cranfield University
  • 2. Outline • • • • • • • • Introduction Exercise- Thinking about our current practice Setting the stage What does the research indicate Lets hear from an Expert FAQ’s – Responding to the viewers Exercise Research Based Best Practices for Multimedia Instructional Design • Individual Learner Characteristics • Putting the research into practice online – One Potential Application • Conclusion and Open Discussion
  • 3. Some Facts About Learning Styles • The concept of “cognitive styles” originated in the 1930’s (Allport) • Research on “learning style” emerged in the early 1960’s • By 2006, over 650 books on learning styles have been published in the U.S. and Canada • Over 4,500 articles have been written about learning styles in professional publications • Over 26,000 web sites are available for measuring and addressing learning styles
  • 4. Breaking the Ice • Think about how you currently incorporate learning styles into your training and education programs. • Take a few minutes and reflect on the following. How do you currently: 1. Identify individual differences in learners (innate characteristics, tools used to measure a learner’s “style” etc.)? 2. Address those individual differences in your instructional designs? 3. Validate your instructional design to ensure it made a difference at the individual and group level?
  • 5. Setting the Stage COGNITIVE STYLE Learning Preferences LEARNING STYLE Learning Strategy Learner Aptitudes The terms learning style and cognitive style are closely related and are often used interchangeably. Both operate without the individual's awareness and are assumed to be less amenable to change and conscious control.
  • 6. Some working definitions • Cognitive Style: An innate habitual approach to processing information when engaging in cognitive tasks • Learning Style: An innate pattern of thinking, perceiving, problem solving, and remembering when approaching a learning task • Learning Strategy: A chosen plan of action in how to approach a given learning task • Learning Preferences: An expressed personal preference favoring one type of learning environment, method of teaching or instruction over another • Learner Aptitudes: Special innate capacities that give rise to competencies in dealing with specific types of content in the world
  • 7. A Selection of Popular Learning Styles Popular Models of Learning Styles Allinson & Hayes’ Cognitive Styles Index CSI Apter’s Motivational Style Profile MSP Dunn & Dunn’s model and instruments of learning styles - Entwistle’s Approaches and Study Skills Inventory for Students ASSIST Felder-Silverman Index of Learning Styles ILS Fleming & Mills’ Visual Aural Reading and Kinaesthetic VARK Gardner’s Multiple Intelligences - Gregorc’s Styles Delineator GSD Herrman’s Brain Dominance Instrument HBDI Honey & Mumford’s Learning Styles Questionnaire LSQ Jackson’s Learning Styles Profiler LSP Kolb’s Learning Style Inventory LSI Myers-Briggs Type Indicator MBTI Riding’s Cognitive Styles Analysis CSA Sternberg’s Thinking Styles Inventory TSI Vermunt’s Inventory of Learning Styles ILS
  • 8. Multiple Intelligences (Gardner) • • • • • • • • Verbal linguistic Logical-mathematical Musical Spatial Bodily Kinaesthetic Interpersonal Intrapersonal Naturalist
  • 9. VARK (Fleming & Mills) Sense intake-output preference • • • • Read- Write (Digital): Symbols Aural (Auditory): Sounds Visual: Graphics/Pictures Kinaesthetic: Space/Motion Brain lateralisation theory (left brain and right brain Deductive Left Right Read/Write (Digital) Visual Inductive Aural (Auditory) Kinaesthetic
  • 10. Learning Styles Inventory (Kolb) Accomodators Grasp: concrete experience Transform: active experimentation Doing Sensing Conflict Convergers Grasp: abstract conceptualisation Transform: active experimentation Divergers Grasp: concrete experience Transform: reflective observation Watching Assimilators Grasp: abstract conceptualisation Transform: reflective observation Thinking
  • 11. MBTI (Myers & Briggs) • Based on Jung’s observation that differences in behavior result from inborn tendencies to use the mind in different ways • Combination of personality modes (E, I, J, P) and cognitive modes (S, N, T, F) Extraversion (E) V Introversion (I) Sensing (S) V Intuition (N) Thinking (T) V Feeling (F) Judging (J) V Perception (P)
  • 12. What the Research Says Tool/Instrument Style Validity/Impact* Cognitive Styles Index (CSI Intuition-Analysis Undetermined Gregoric Style Delineator (GSD) Concreteabstract/sequential – random Questionable Learning Styles Inventory (LSI) Experiential learning model Questionable Inventory of Learning Styles (ILS) Depth of processing meaning, production Questionable Myers Briggs Type Indicator (MBTI) 16 Personality Types Low * The validity of each tool with respect to instructional impact is based on current psychometric research consensus.
  • 13. Coffield et al. (2004): 13 from original 71 models Coffield, F., Moseley, D., Hall, E. & Ecclestone, K. (2004) Learning styles and pedagogy in post-16 learning: A systematic and critical review.
  • 14. Do you remember the ATI research ?
  • 15. What about Other Scientific Research ? - $1000 challenge
  • 16. Lets hear from an Expert Professor Daniel Willingham Describes research showing that learning styles are a myth
  • 17. FAQ’s About Learning Styles
  • 18. How can you not believe that that people learn differently? Isn’t it obvious? • People do learn differently, but I think it is very important to say exactly how they learn differently, and focus our attention on those differences that really matter. If learning styles were obviously right it would be easy to observe evidence for them in experiments. Yet there is no supporting evidence. • There are differences among kids that both seem obvious to us and for which evidence is easily obtained in experiments, e.g., that people differ in their interests, that students vary in how much they think of schoolwork as part of their identity (“I’m the kind of kid who works hard in school”) and that kids differ in what they already know at the start of a lesson.
  • 19. Learning Style versus Learning Ability – What does it matter? • The idea that people differ in ability is not controversial— everyone agrees with that. Some people are good at dealing with space, some people have a good ear for music, etc. • So the idea of “style” really ought to mean something different. If it just means ability, there’s not much point in adding the new term.
  • 20. All right then, what do you think is the difference between style and ability? • Ability is that you can do something. • Style is how you do it. • Thus, one would always be happy to have more ability, but different styles should be equally desirable. I find a sports analogy useful here. Two basketball players may be of equal ability, but have different styles on the court, one being a risk-taker, and the other quite conservative in his play. • Sometimes people say it’s obvious that there are learning styles because blind and deaf people learn differently. This is a difference in ability, not style.
  • 21. I thought there was no good evidence, not that the evidence proved that learning styles don’t exist! So why do you say they don’t exist? The review (Pashler, H., McDaniel, M., Rohrer, D. & Bjork, R. 2008. Learning styles: Concepts and evidence did conclude just that. The ideal experiment has not been conducted. A lot of less-thanideal experiments have been conducted, and they are not promising for learning styles theories at all.
  • 22. Two important points to keep in mind when evidence for a theory is lacking: (1) it’s absolutely true that we could find out tomorrow that there are learning styles after all.. Note this is always the case--you can't absolutely prove a theory untrue. But as things stand, there’s no scientific reason to think that the theories that have been proposed are correct; (2) the fact that we haven’t definitively proven a theory wrong seems like a poor reason to advocate using the theory in classrooms.
  • 23. Exercise • If learning styles can’t be proven, what does this mean for your instructional design? (15 Minutes) • Break into groups of three and consider what research based practice could we potentially use to improve instruction and multimedia content delivery. • Record your top three ideas and present them back to the group
  • 24. Dr. Richard Felder • Still remains a proponent of Learning Styles • Views learning styles more as individual preferences • Advocates appealing to students using good instructional design / effective pedagogy
  • 25. Using Effective Pedagogy • Teaching to address all categories of a learning styles model is not a radical idea, and specific suggestions for how to do it should look familiar to anyone who has studied the literature of effective pedagogy. • Don't just lecture—provide opportunities in class for both practice in course-taught methods (for the active learners) and reflection on the outcomes (for the reflective learners). • Teach basic principles and theories (which intuitive learners are comfortable with), but only in the context of their real-world applications and with numerous examples of how to apply them (without which many sensors may have difficulty grasping the underlying concepts).
  • 26. Using Effective Pedagogy • Provide information both visually (pictures, diagrams, flow charts, concept maps, demonstrations,…) and verbally (written and spoken explanations) rather than making almost everything verbal (as is usually done except in art and architecture courses). • Teach new course material in a logical and systematic way (which thinkers and sequential learners need), but be sure to show how it connects to the students' prior knowledge and experience and to problems of global and social importance (for feelers and global learners).
  • 27. Using a balanced perspective • Learning styles are not either-or categories, but preferences that may be mild, moderate, or strong. The fact that students may be classified as, say, sensing learners, says nothing about either their intuitive skills or their sensing skills. It follows that students with any learning style can succeed in any career or endeavor. • Both logic and published research suggest that students taught in a manner matched to their learning style preferences tend to learn more than students taught in a highly mismatched manner. It does not follow, however, that matching instruction to fit students’ learning styles is the optimal way to teach. For one thing, it is impossible if more than one learning style is represented in a class.
  • 28. Where the rubber hits the road • The optimal teaching style strikes a balance (not necessarily an equal one) between the poles of each dimension of the chosen learning styles model. When this balance is achieved, all students are taught sometimes in their preferred mode. • The ideal balance among learning style categories depends on the subject, level, and learning objectives of the course and the backgrounds and skills of the students. Part of the instructor’s job is to attempt to ascertain that ideal and to teach in a manner that comes as close to it as possible.
  • 29. Research-based Best Practices for Instructional Design • • • • • The work of Ruth Clark and Richard E. Mayer Learning: three metaphors Constructing mental representations Eight principles for using multimedia Beyond the principles
  • 30. Three Metaphors of Learning: Response strengthening • Learning is strengthening or weakening of associations • Learner is passive recipient of rewards and punishments • Instructor is dispenser of rewards and punishments Source:, photo by: Sgt. Aaron Rooks
  • 31. Three Metaphors of Learning: Information Acquisition • Learning is adding information to memory • Learner is passive recipient of information • Instructor is dispenser of information At School in the Year 2000 (Villemard, 1910)
  • 32. Three Metaphors of Learning: Knowledge Construction • Learning is building a mental representation • Learner is active sense maker • Instructor is Cognitive Guide Sacagawea with Lewis and Clark during their expedition of 1804-06 (colour litho) by Wyeth, Newell Convers (1882-1945)
  • 33. Clark and Lewis’ ‘Representation’ (1814)
  • 34. Mental Representation (AKA ‘concept’) Source: Source: Source
  • 35. Cognitive Theory of Multimedia Learning (Mayer, 2005) Multimedia Presentation Words Working Memory Senses Ears Selecting Words Sounds Organizing Words Long-Term Memory Verbal Mode Integrating Pictures Eyes Selecting Images Images Organizing Images Pictorial Mode ‘Meaningful learning occurs when the learner appropriately engages in all of these processes’ (Clark & Mayer, 2011, p.37) Prior Knowledge
  • 36. Eight Multimedia Principles … • Multimedia • Use words and graphics rather than words alone • Contiguity • Align words to corresponding graphics • Modality • Present words as audio narration rather than on-screen text • Redundancy • Explain visuals with words in audio or text: not both
  • 37. Eight Multimedia Principles … • Coherence • Adding material can hurt learning • Personalisation • Use conversational style and virtual coaches • Segmenting and Pretraining • Managing complexity by breaking a lesson into parts
  • 38. Summary of Research Results from the Eight Multimedia Principles Principle Median Effect Size Number of Tests with Effects Greater than .5 Multimedia 1.50 9 of 9 Contiguity 1.11 8 of 8 Coherence 1.32 10 of 11 Modality .97 20 of 21 Redundancy .69 8 of 10 Personalization 1.30 10 of 10 Segmenting .98 3 of 3 Pretraining .92 7 of 7 Source: Clark & Mayer (2011)
  • 39. Beyond the principles … • Worked examples • Practice • Collaborative learning • Learner control versus program control • Thinking skills • Simulations and games
  • 40. Knowledge Structures & Graphic Support Type of Cognitive Structure Description Graphic Representation Flow chart Example Process Explain a cause-and-effect chain Explanation of how the human ear works Comparison Compare and contrast two Matrix or more elements along several dimensions Comparison of two theories of learning with respect to nature of the learner, teacher, and instructional methods Generalization Describe main idea and supporting details Branching tree Presentation of thesis for the major causes of the American Civil War along with evidence Enumeration Present a list of items List List of the names of seven principles of multimedia design Classification Analyze a domain into sets Hierarchy and subsets Description of a biological classification system for see animals
  • 41. General Multimedia Design Principles for Text and Illustrations Principle Description Concentrated The key ideas are highlighted in the illustrations and in the text Concise Extraneous descriptions are minimized in the text and extraneous visual features are minimized in the illustrations Correspondent Corresponding illustrations and text segments are presented near each other on the page Concrete The text and illustrations are presented in ways that allow for easy visualisation Coherent The presented material has a clear structure (e.g., a cause-andeffect chain) Comprehensible The text and illustrations are presented in ways that are familiar and allow the learner to apply relevant past experience Codable Key terms used in the text and key features of the illustration are used consistently and in ways that make them more memorable
  • 42. Learner Characteristics
  • 43. Learner Characteristics (empirically validated) • Schemas - Prior knowledge and experience along with associated schemas are indisputably the biggest factors in predicting a learner’s initial success in almost every learning situation. • Amount of invested mental effort - A highly motivated learner will learn just about anything despite inadequacies in instructional design. Highly motivated learners will often excel in settings where instructional resources are readily accessible.
  • 44. Additional Learner Characteristics (empirically validated) • Perceived self efficacy - Low perceived self-efficacy can function as a potential internal distraction. If cognitive resources are consumed with managing negative states associated with an instructional task, learning will be negatively impacted. • Aptitudes - In Howard Gardner’s book, Frames of Mind: the Theory of Multiple Intelligences, he identifies seven aptitude like traits which he refers to as “intelligences.” Although these aptitudes are mainly biologically and environmentally determined, their interaction with instructional methods and content is largely situational.
  • 45. Putting the promise into action • Part of the original MLS challenge was to provide interventions in the delivery of content (multi channel learning) to suit the learners needs. • From the presentation so far we know that we know that sound instructional design principles can influence student achievement but what do we do about using the answers with respect to individual learner characteristics in an automated environment? • One suggestion is to look at computer based tutoring systems
  • 46. Push for Tailored Training Computer-based tutoring systems (CBTS) have demonstrated significant promise in tutoring individuals in well-defined domains, but… Fifty years of research have been unsuccessful in making CBTS ubiquitous in military training… Why? CBTS are expensive to author and are insufficiently adaptable to support the tailored, self-regulated , individual & small unit tutoring experiences required to support: • U.S. Army Learning Model (ALM) for 2015 (TRADOC, 2011) • U.S. Air Force (AETC, 2008) • U.S. Navy STEM Grand Challenge (ONR, 2012) • OSD R&T Vision for PAL • NATO HFM RTG 237 (Advanced ITS) • TTCP HUM TP-2 (Training Panel)
  • 47. Why Computer-Based Tutoring Systems (CBTS) • ITSs apply Artificial Intelligence tools and methods to individualize instruction • Based on benefits associated with one-on-one expert tutoring (2-Sigma Problem; Bloom, 1984) • Mediates learning by providing feedback when appropriate and adjusting difficulty levels to maintain desired challenge. 47
  • 48. Individual Tutoring Systems – Proven Results • VanLehn (2011): • 27 Evaluations • -Effect size of 0.59 overall • -Effect size of 0.76 for step-based tutoring • -Effect size of 0.40 for substep-based tutoring • Kulik/Fletcher (2012): • 45 “Systems Evaluations” • -Effect size of 0.60 overall • -Effect size of 0.75 for 39 properly aligned studies
  • 49. Overall Intent of GIFT (Generalized Intelligent Framework for Tutoring )
  • 50. Generalized Intelligent Framework for Tutoring (GIFT)
  • 51. Pedagogical Modeling • Designed to balance the level of guidance a learner needs with the goal of maintaining engagement and motivation
  • 52. Application of GIFT vMedic will drive the Intelligent Tutoring behaviors within GIFT which in turn, will drive a number of instructional interventions within “vMedic”.
  • 53. Recommendations • Select instructional methods and media that match the nature of the content to be taught (i.e., use graphics for content material that is predominately visual in nature, and verbal/textual media for content that is more abstract and declarative in nature). • Recognize that most learners are adaptable and cognitively flexible, especially if motivated. You don’t need to overcompensate for a hypothesized innate trait that—in many instances—may not be valid.
  • 54. Recommendations • Supplement your learning “styles” paradigm with other learner attributes that have been tried, tested, and proven true (prior knowledge, motivation, aptitudes, and learner confidence related to the content or task to be learned). • Recognize that the concept of learning styles is very appealing and has somehow become an integral part of our education and training folklore. How strongly one feels about a particular belief is no justification for ignoring the hard scientific evidence.
  • 55. Primary References: Cassidy, S. (2004). Learning styles: An overview of theories, models, and measures. Educational Psychology, 24(4), 419. Clark, R. C., & Mayer, R. E. (2011). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. Pfeiffer. Felder, R. (2010) ARE LEARNING STYLES INVALID? (HINT: NO!) Retrieved from: felder/public/Papers/LS_Validity(OnCourse).pdf Howles, L. (2007). Learning Styles: How to Apply the Latest Research to Designing eLearning. Retrieved from: Nilson, L. “The Truth about Learning Styles.” Keynote at the International Lilly Conference on College Teaching. Oxford, OH. 18-21 November 2010 Pashler, H., McDaniel, M., Rohrer, D., and Bjork, R. (2008). Learning Styles. Psychological Science in the Public Interest, 9(3):105-119. Sorden, S. (2005). A cognitive approach to instructional design for multimedia learning. Informing Science Journal, 8, 263-279. Available at Sottilare, R., Brawner, K., Goldberg, B., and Holden, K. (2012). The Generalized Intelligent Framework for Tutoring (GIFT). Retrieved from: